Introduction to Deep Learning

eScience Center Digital Skills Programme

This workshop gives an introduction to deep learning for researchers who are familiar with the basics of (non-deep) machine learning.

This is an hands-on introduction to the first steps in Deep Learning, intended for researchers who are familiar with (non-deep) Machine Learning.The use of Deep Learning has seen a sharp increase of popularity and applicability over the last decade. While Deep Learning can be a useful tool for researchers from a wide range of domains, taking the first steps in the world of Deep Learning can be somewhat intimidating. This introduction aims to cover the basics of Deep Learning in a practical and hands-on manner, so that upon completion, you will be able to train your first neural network and understand what next steps to take to improve the model.We start with explaining the basic concepts of neural networks, and then go through the different steps of a Deep Learning workflow. Learners will learn how to prepare data for deep learning, how to implement a basic Deep Learning model in Python with Keras, how to monitor and troubleshoot the training process and how to implement different layer types such as convolutional layers.The workshop is based on the teaching style of the Carpentries, and learners will follow along while the instructors write the code on screen. More information can be found on the <a href="https://esciencecenter-digital-skills.github.io/2024-02-05-ds-dl-intro/" title="https://esciencecenter-digital-skills.github.io/2024-02-05-ds-dl-intro/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">workshop website</a>. Cancellation and No-Show PolicyPlease be advised that by signing up, you agree to our Cancellation and No-Show Policy, which states that cancellations made less than 2 workings days prior to the event will incur a no-show fee. Please read the full policy <a href="https://www.esciencecenter.nl/cancellation-and-no-show-policy-for-escience-center-training-events/" title="https://www.esciencecenter.nl/cancellation-and-no-show-policy-for-escience-center-training-events/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">here</a> for more details. AudienceThe workshop is aimed at PhD candidates and other researchers or research software engineers. We offer tickets for researchers who are affiliated with Dutch research institutions. We also offer industry tickets for attendees who are not affiliated with Dutch research institutions. We do not accept registrations by Master students. Prerequired knowledgeLearners are expected to have the following knowledge:<ul><li>Basic Python programming skills and familiarity with the Pandas package.</li><li>Basic knowledge on Machine learning, including the following concepts: Data cleaning, train &amp; test split, type of problems (regression, classification), overfitting &amp; underfitting, metrics (accuracy, recall, etc.).</li></ul>Note that this workshop is an introduction into deep learning. If you are already familiar with the concepts in the syllabus then a more advanced course might be better suited for your interests! SyllabusIntroduction<ul><li>Recall the sort of problems for which Deep Learning is a useful tool</li><li>List some of the available tools for Deep Learning</li><li>Recall the steps of a Deep Learning workflow</li><li>Identify the inputs and outputs of a deep neural network</li><li>Explain the operations performed in a single neuron</li><li>Test that you have correctly installed the Keras, Seaborn and Sklearn libraries</li><li>Describe what a loss function is</li></ul>Classification by a Neural Network using Keras<ul><li>Use the deep learning workflow to structure the notebook</li><li>Explore the dataset using pandas and seaborn</li><li>Use one-hot encoding to prepare data for classification in Keras</li><li>Describe a fully connected layer</li><li>Implement a fully connected layer with Keras</li><li>Use Keras to train a small fully connected network on prepared data</li><li>Interpret the loss curve of the training process</li><li>Use a confusion matrix to measure the trained networks’ performance on a test set</li></ul>Monitor the training process<ul><li>Explain the importance of keeping your test set clean, by validating on the validation set instead of the test set</li><li>Use the data splits to plot the training process</li><li>Explain how optimization works</li><li>Design a neural network for a regression task</li><li>Measure the performance of your deep neural network</li><li>Interpret the training plots to recognize overfitting</li><li>Use normalization as preparation step for Deep Learning</li><li>Implement basic strategies to prevent overfitting</li></ul>Advanced layer types<ul><li>Understand why convolutional and pooling layers are useful for image data</li><li>Implement a convolutional neural network on an image dataset</li><li>Use a drop-out layer to prevent overfitting</li></ul>Outlook<ul><li>Understand that what we learned in this course can be applied to real-world problems</li><li>Use best practices for organising a deep learning project</li><li>Identify next steps to take after this course</li></ul> WhereThis training will take place online. The instructors will provide you with the information you will need to connect to this meeting.

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Introduction to Deep Learning

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Introduction to Deep Learning

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